A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling

Author:

Guo Jie1ORCID,Yan Hao2ORCID,Zhang Chen1ORCID

Affiliation:

1. Tsinghua University, Beijing, China

2. Arizona State University, Tempe, AZ

Funder

NSFC

BNSF

NSF

DMS

CMMI

ASFC

Tsinghua University Intelligent Logistics & Supply Chain Research Center

Division of Civil, Mechanical and Manufacturing Innovation

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Modeling and Simulation,Statistics and Probability

Reference52 articles.

1. Agrawal, S., and Goyal, N. (2012), “Analysis of Thompson Sampling for the Multi-Armed Bandit Problem,” in Conference on Learning Theory, pp. 39–1.

2. Posterior Bayes Factors

3. A Generalized Least-Square Matrix Decomposition

4. Composite Gaussian process models for emulating expensive functions

5. Generic Outlier Detection in Multi-Armed Bandit

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